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To propose a generic method to simplify the fuzzy logic‐based failure mode and effect analysis (FMEA) methodology by reducing the number of rules that needs to be provided by FMEA users for the fuzzy risk priority number (RPN) modeling process.

The fuzzy RPN approach typically requires a large number of rules, and it is a tedious task to obtain a full set of rules. The larger the number of rules provided by the users, the better the prediction accuracy of the fuzzy RPN model. As the number of rules required increases, ease of use of the model decreases since the users have to provide a lot of information/rules for the modeling process. A guided rules reduction system (GRRS) is thus proposed to regulate the number of rules required during the fuzzy RPN modeling process. The effectiveness of the proposed GRRS is investigated using three real‐world case studies in a semiconductor manufacturing process.

In this paper, we argued that not all the rules are actually required in the fuzzy RPN model. Eliminating some of the rules does not necessarily lead to a significant change in the model output. However, some of the rules are vitally important and cannot be ignored. The proposed GRRS is able to provide guidelines to the users which rules are required and which can be eliminated. By employing the GRRS, the users do not need to provide all the rules, but only the important ones when constructing the fuzzy RPN model. The results obtained from the case studies demonstrate that the proposed GRRS is able to reduce the number of rules required and, at the same time, to maintain the ability of the Fuzzy RPN model to produce predictions that are in agreement with experts' knowledge in risk evaluation, ranking, and prioritization tasks.

The proposed GRRS is limited to FMEA systems that utilize the fuzzy RPN model.

The proposed GRRS is able to simplify the fuzzy logic‐based FMEA methodology and make it possible to be implemented in real environments.

The value of the current paper is on the proposal of a GRRS for rule reduction to enhance the practical use of the fuzzy RPN model in real environments.

An inherent problem on risk priority number (RPN) value duplication of traditional failure modes and effect analysis (FMEA) also exists in two customer-oriented FMEAs. One has no unique value, and another has 1% unique values out of 4,000 possible values. The RPN value duplication has motivated the development of a new customer-oriented FMEA presented in this paper to achieve practically all 4,000 unique values and delivering reliable prioritization.

The drastic improvement is the result of power-law and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). By having all three risk factors in a power-law form, all unique values can be obtained, and by applying VIKOR to these power-law terms, the prioritization is more practical and reliable.

The proposed VIKOR power law-based customer-oriented FMEA can achieve practically all 4,000 unique values and is tested with two case studies. The results are more logical than the results from the other two customer-oriented FMEAs.

The evaluation has been done on two case studies for the service sector. Therefore, additional case studies in other industrial sectors will be required to confirm the effectiveness of this new customer-oriented RPN calculation.

Achieving all 1,000 unique values could only be done by having experts tabulate all possible combinations for the traditional FMEA. Therefore, achieving all 4,000 unique values will be much more challenging. A customer-oriented FMEA has been developed to achieve practically all 4,000 unique risk priority numbers, and that the prioritization is more practical and reliable. Furthermore, it has a connection to the traditional FMEA, which helps explain the traditional one from a broader perspective.

The purpose of this paper is to propose a cost-effective, time-saving and easy-to-use failure modes and effects analysis (FMEA) system applied on the quality control of supplied products. The traditional FMEA has been modified and adapted to fit the quality control features and requirements. The paper introduces a new and revised FMEA approach, where the “failure concept” has been modified with “defect concept.”

The typical FMEA parameters have been modified, and a non-linear scale has been introduced to better evaluate the FMEA parameters. In addition, two weight functions have been introduced in the risk priority number (RPN) calculus in order to consider different critical situations previously ignored and the RPN is assigned to several similar products in order to reduce the problem of complexity.

A complete procedure is provided in order to assist managers in deciding on the critical suppliers, the creation of homogeneous families overcome the complexity of single product code approach, in RPN definition the relative importance of factors is evaluated.

This different approach facilitates the quality control managers acting as a structured and “friendly” decision support system: the quality control manager can easily evaluate the critical situations and simulate different scenarios of corrective actions in order to choose the best one. This FMEA technique is a dynamic tool and the performed process is an iterative one. The method has been applied in a small medium enterprise producing hydro massage bathtub, shower, spas and that commercializes bathroom furniture. The firm application has been carried out involving a cross-functional and multidisciplinary team.

The purpose of this paper is to present a reliability centered maintenance (RCM) embedded integer linear programming approach (suited to the budget monetary resources allocation task) to the maintenance strategies mix selection for an industrial plant equipment.

The developed approach allows to determine the optimal maintenance strategies mix for a set of equipment in a more quantitative way than the classic RCM approach. The proposed model takes into account, for each potential failure determined using the FMECA and for each admissible strategy, the costs and the potential risk priority number (RPN) reduction. Finally, an industrial case concerning an Italian paper-mill plant is reported to demonstrate the effectiveness of the approach presented.

The paper finds that the application of the proposed approach allows to optimally allocate the budget monetary resources, determining which suitable maintenance practice apply to each failure, taking into account the costs of each strategy and the potential reduction of the RPN.

The proposed model permits to assign (during the budget monetary resources allocation task) to each failure the optimal strategy, among a set of suitable maintenance practices, considering the costs and the estimated RPN reduction.

The paper proposes a completely new RCM embedded approach to the maintenance strategies selection, in order to optimally allocate the budget monetary resources. This model overcomes the limits of the traditional RCM approach, taking into account quantitative aspects, i.e. the compatibility constraint between failures and policies, the maintenance strategies costs, and the RPN estimated reduction.

In almost all of the existing resources of failure mode and effect analysis (FMEA), “severity” is being determined from the designers’ point of view, not from the customers’ side. In this paper, a new approach is proposed to enhance FMEA capabilities through its integration with Kano model. This evolves the current approaches for determination of severity and “risk priority number” (RPN) through classifying severities according to customers’ perceptions. It supports the nonlinear relationship between frequency and severity of failure. Also a new index called “correction ratio” (Cr) is proposed to assess the corrective actions in FMEA. The findings of a short case study highlight the gap between managers and customers in prioritising a set of failures and the difference between RPN and Cr prioritisations, caused by target failure frequencies. The proposed approach enables managers/designers to prevent failures at early stages of design, based on customers who have not experienced their products/services yet.

One of the key elements of a maintenance system is risk analysis because the risk level of an engineering system will determine the maintenance policy required. Failure Mode and Effect Analysis (FMEA) is widely applied in evaluating risk of failure of engineering systems. However, the conventional FMEA used in analyzing risk by most industries has shortcomings such as an inability to utilize imprecise and interval data in prioritizing risk. To overcome these limitations, different variants of FMEA have been reported in the literature. However, these modified approaches are computationally intensive; hence, the purpose of this paper is to develop an efficient FMEA-based methodology that is easy to analyze and implement.

The proposed technique combines the Taguchi method with FMEA in order to analyze risk of engineering systems easily and effectively. The effectiveness of the approach is demonstrated with a case study of the fuel oil system of a marine diesel engine.

The results of the integrated Taguchi method and FMEA, when compared with well-known techniques, namely, VIKOR and compromise programming, from the literature are very similar. From the comparative analysis, it was evident that the proposed method is a viable option to the more computationally intensive approaches used in the literature.

The approach proposed is novel and simple and can be implemented more easily than approaches from the literature in analyzing risk.

This paper aims to prioritize preventive maintenance actions on process equipment by evaluating the risk associated with failure modes using predictive maintenance data instead of maintenance history alone.

In process plants, maintenance task identification is based on the failure mode and effect analysis (FMEA). To eliminate or mitigate risk caused by failure modes, maintenance tasks need to be prioritized. Risk priority number (RPN) can be used to rank the risk. RPN is estimated invariably using maintenance history. However, maintenance history has deficiencies, like limited data, inconsistency etc. To overcome these deficiencies, the proposed approach uses the predictive maintenance data clubbed with expert domain knowledge. Unlike the traditional single step approach, RPN is estimated in two steps, i.e. Step 1 estimates the “Possibility of failure mode detection” and Step 2 estimates RPN using output of step 1. Fuzzy sets and approximate reasoning are used to handle the uncertainty/imprecision in data and subjectivity/vagueness of expert domain knowledge. Fuzzy inference system is developed using MATLAB® 6.5.

The proposed approach is applied to a large gearbox in an integrated steel plant. The gearbox is covered under a predictive maintenance program. RPN for each of the failure modes is estimated with the proposed approach and compared with the maintenance task schedule. The illustrative case study results show that the proposed approach helps in detection of failure modes more scientifically and prevents “Over maintenance” to ensure reliability.

This approach gives an opportunity to integrate the predictive maintenance data and subjective/qualitative domain expertise to evaluate the possibility of failure mode detection (POD) quantitatively, which is otherwise purely estimated using subjective judgments. The approach is generic and can be applied to a variety of process equipment to ensure reliability through prioritized maintenance scheduling.

This paper aims to develop a system dynamics (SD) model to identify causal relationships among the elements of failure modes and effects analysis (FMEA), i.e. failure modes, effects and causes.

A causal loop diagram (CLD) has been developed based on the results obtained from interdependencies and correlations analysis among the FMEA elements through applying the integrated approach of FMEA-quality function deployment (QFD) developed by Shaker et al. (2019). The proposed model was examined in a steel manufacturing company to identify and model the causes and effects relationships among failure modes, effects and causes of a roller-transmission system.

Findings indicated interactions among the most significant failure modes, effects and causes. Moreover, corrective actions defined to eliminate or relieve critical failure causes. Consequently, production costs decreased, and the production rate increased due to eliminated/decreased failure modes.

The application of CLD illustrates causal relationships among FMEA elements in a more effective way and results in a more precise recognition of the root causes of the potential failure modes and their easy elimination/decrease. Therefore, applying the proposed approach leads to a better analysis of the interactions among FMEA elements, decreased system's failure rate and increased system availability.

The literature review indicated a few studies on the application of SD methodology in the maintenance area, and no study was performed on the causal interactions among FMEA elements through an FMEA-QFD based SD approach. Although the interactions of these elements are significant and helpful in risks ranking, researchers fail to investigate them sufficiently.

The purpose of this study is to propose a state‐of‐the‐art new approach to enhance FMEA assessment capabilities.

Through data envelopment analysis (DEA) technique and its extension, the proposed approach evolves the current rankings for failure modes by exclusively investigating SOD in lieu of RPN and to furnish improving scales for SOD.

Through an illustrative example the proposed approach supports the proposition that DEA can not only complement traditional FMEA for improving assessment capability but also, especially, provide corrective information regarding the failure factors – severity, occurrence and detection. Further application of DEA Stratification also reveals that this methodology is useful for managing resource allocation and risk management.

It is shown that the proposed approach enables manager/designers to prevent system or product failures at an early stage of design. Moreover, the approach is able to provide managerial insight of SOD more effectively than justifying the efforts on RPN alone. Projection of each SOD is determined to help managers examine the scale of efforts. Finally, the stratification analysis offers the economical allocation of failure modes with respect to the incurred costs and the efficiency.

The paper proposes a unique new approach, robust, structured and useful in practice, for failure analysis. The methodology, within a firmed methodology, overcomes some of the largely known shortfalls of traditional FMEA: it takes into account multiple criteria and restricted weighted; and it analyses the failure modes' ranking considering not only the direct impacts of failure indices, but also the contribution of these indices.

Proposes an improvement of the RPN model used in the failure mode and effects analysis. The aim of the proposed model is to make RPN a better measurement tool. Suggests combining the improved RPN model with an expected cost model in quality improvement efforts.